Robust networked power system load frequency control against hybrid cyber attack
Bibliographic record
Abstract
Abstract Modern network and communication technologies are essential for the implementation and operation of load frequency control (LFC) systems. The measurements of crucial LFC system parameters will be compromised by attackers, rendering data received by the defence inaccurate and causing frequency fluctuations or even system collapse. To detect the potential attack on measured data and keep the LFC performance, an adaptive event‐triggered scheme with fractional order global sliding mode control scheme is proposed in this paper. Furthermore, Markov theory is employed for the modelling process with energy storage to present a multi‐area LFC power system considering renewable energy and hybrid cyber attacks. Stability and stabilisation criteria are built by employing improved Lyapunov stability theory and second‐order Bessel‐Legendre inequality. Finally, a two‐area LFC system under hybrid cyber attacks and a modified IEEE 39‐bus New England test power system with 3 wind farms are simulated to explore the efficacy of the proposed method.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".